To apply stereo matching for monocular depth estimation, Luo et al. [1]} proposed a two-stage pipeline that decomposes monocular depth estimation into two stages, view synthesis and stereo matching respectively. In their approach, a second view is first synthesized and fed together with the original view to the stereo matching stage to compute the disparity. Stereo matching can leverage more geometric constraints into the network training, thus reducing the demand for ground truth depth. More recent studies [2]}, [3]} improved upon this idea and achieved promising performances. These two-stage methods [1]}, [2]}, [3]}, are mainly designed for perspective images. In 360 domain, most of the recent studies [7]}, [8]}, [9]}, [10]}, [11]}
still follow the same single-stage monocular estimation procedure with adaptation to 360 We use the terms 360, omnidirectional, equirectangular, spherical interchangeably in this paper geometry.
